4.4 Review

Potential Ocular Biomarkers for Early Detection of Alzheimer's Disease and Their Roles in Artificial Intelligence Studies

Journal

NEUROLOGY AND THERAPY
Volume 12, Issue 5, Pages 1517-1532

Publisher

SPRINGER LONDON LTD
DOI: 10.1007/s40120-023-00526-0

Keywords

Alzheimer's; Artificial intelligence; Early detection; Mild cognitive impairment; Ocular biomarkers

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Alzheimer's disease is the leading cause of dementia worldwide, and early detection is crucial for disease management. Eye biomarkers, especially eye movement tracking, have the potential to detect early Alzheimer's disease. However, there is currently no definitive eye biomarker for early Alzheimer's disease. Studies using artificial intelligence with eye biomarkers have shown promising results in improving the accuracy of identifying Alzheimer's disease patients, which may serve as a screening tool for early detection in older patients.
Alzheimer's disease (AD) is the leading cause of dementia worldwide. Early detection is believed to be essential to disease management because it enables physicians to initiate treatment in patients with early-stage AD (early AD), with the possibility of stopping the disease or slowing disease progression, preserving function and ultimately reducing disease burden. The purpose of this study was to review prior research on the use of eye biomarkers and artificial intelligence (AI) for detecting AD and early AD. The PubMed database was searched to identify studies for review. Ocular biomarkers in AD research and AI research on AD were reviewed and summarized. According to numerous studies, there is a high likelihood that ocular biomarkers can be used to detect early AD: tears, corneal nerves, retina, visual function and, in particular, eye movement tracking have been identified as ocular biomarkers with the potential to detect early AD. However, there is currently no ocular biomarker that can be used to definitely detect early AD. A few studies that used AI with ocular biomarkers to detect AD reported promising results, demonstrating that using AI with ocular biomarkers through multimodal imaging could improve the accuracy of identifying AD patients. This strategy may become a screening tool for detecting early AD in older patients prior to the onset of AD symptoms.

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